Visualization
SNA Week 6
Matt Pietryka
Why we visualize networks
Basic visualization options
Visualization norms
Colors in R plots: Basics
par(mar = c(0, 0, 0, 0))
x <- 1:10
plot(x = x, y = rep(5, 10),
pch=19, # SHAPE
cex=3, # SIZE
col="dark red" # COLOR
)
points(x = x, y = rep(6, 10), pch=19, cex=3, col="557799")
points(x = x, y = rep(4, 10), pch=19, cex=3, col=rgb(.25, .5, .3))
Set opacity with alpha
par(mar = c(0, 0, 0, 0))
alpha_vals <- seq(0, 1, by = 0.2)
plot(x = 1:6, y = rep(5,6),
pch = 19,
cex = 20,
col = rgb(.25, .5, .3, alpha = alpha_vals),
xlim = c(0,7))
text(x = 1:6, y = rep(4, 6), labels = alpha_vals, cex = 2)
Set alpha for hex colors, change background color
par(mar = c(0, 0, 0, 0)) # MARGINS
par(bg = "gray40") # BACKGROUND COL.
col.tr <- grDevices::adjustcolor("557799", alpha = 0.4) # ADD ALPHA
plot(x = 1:6, y = rep(5,6),
pch = 19, cex = 20, col= col.tr, xlim = c(0,7)) 
Can gernerate palettes from predefined sets
pal1 <- heat.colors(5, alpha = 1) # the 'heat' palette, opaque
pal2 <- rainbow(5, alpha = .5) # the 'rainbow' palette, transparent
par(mar = c(0, 0, 0, 0)) # MARGINS
par(mfrow = c(1, 2)) # 1 x 2 WINDOW
plot(x=1:10, y=1:10, pch=19, cex=8, col=pal1, axes=FALSE )
plot(x=1:10, y=1:10, pch=19, cex=8, col=pal2, axes=FALSE)
Can gernerate new palettes
# CREATE NEW FUNCTION TO GENERATE COLORS FROM RED TO WHITE TO BLUE
gen_pal_rb <- colorRampPalette(c("red", "blue"))
pal3 <- gen_pal_rb(10) # GENERATE 10 COLORS
# CREATE NEW FUNCTION TO GENERATE COLORS FROM RED TO WHITE TO BLUE
gen_pal_rwb <- colorRampPalette(c("red", "white", "blue"))
pal4 <- gen_pal_rwb(10) # GENERATE 10 COLORS
par(mar = c(0, 0, 0, 0)) # MARGINS
par(mfrow = c(1, 2)) # 1 x 2 WINDOW
plot(x=1:10, y=1:10, pch=19, cex=8, col=pal3, axes=FALSE )
plot(x=1:10, y=1:10, pch=19, cex=8, col=pal4, axes=FALSE )
Use ‘RColorBrewer’ package to find good palettes
library('RColorBrewer')
display.brewer.all()
Use RColorBrewer package to find good palettes
par(mar = c(0, 0, 0, 0)) # MARGINS
display.brewer.pal(8, "Set3")
display.brewer.pal(8, "Spectral")
display.brewer.pal(8, "Blues")
Use RColorBrewer package to find good palettes
par(mfrow = c(1, 2)) # 1 x 2 WINDOW
par(mar = c(0, 0, 0, 0)) # MARGINS
pal5 <- brewer.pal(10, "Set3") # CREATE PALETTE
plot(x=10:1, y=10:1, pch=19, cex=10, col= pal5 , axes=FALSE)
plot(x=10:1, y=10:1, pch=19, cex=10, col= rev(pal5) , axes=FALSE)
Or use http://paletton.com to find good palettes
CREATE IGRAPH NETWORK
# CREATE IGRAPH NETWORK
advice_net <- graph_from_data_frame(d = edge_advice, # EDGE LIST
vertices = node_raw, # NODE ATTRIBUTES
directed = TRUE # DIRECTED EDGES
)
par(mar = c(0, 0, 0, 0))
plot(advice_net)
Can specify node & edge options in the plot() function
- curved edges allow you to see multiple links between two nodes
par(mar = c(0, 0, 0, 0))
plot(advice_net,
edge.arrow.size = .4, # reduce arrow size
edge.curved = .1 # curved edges
) 
par(mar = c(0, 0, 0, 0))
plot(advice_net, edge.arrow.size=.4, edge.color="orange",
vertex.color="orange", vertex.frame.color="orange",
vertex.label=NA # NO NODE LABELS
) 
Can specify node & edge options by adding them to the igraph object
par(mar = c(0, 0, 0, 0))
plot(advice_net) 
Can specify node & edge options by adding them to the igraph object
par(mar = c(0, 0, 0, 0))
plot(advice_net)
legend(x=0.51, y=-.2, c("Harvard/Yale","UConn", "Other"), pch=21,
col="#777777", pt.bg=colrs, pt.cex=2, cex=.8, bty="n", ncol=1)
Color the edges of the graph based on their source node color
# We can get the starting and ending nodes for each edge with the `ends()` function
edge.start <- ends(advice_net,
es = E(advice_net), # EDGES TO RETURN
names = FALSE # RETURN IDs, NOT NAMES
)[,1] # RETURN ONLY THE STARTING NODE
edge.col <- V(advice_net)$color[edge.start]
par(mar = c(0, 0, 0, 0))
plot(advice_net, edge.color = edge.col, edge.curved = .1) 
net.bg <- sample_pa(80) # Barabasi-Albert model
V(net.bg)$size <- 8
V(net.bg)$frame.color <- "white"
V(net.bg)$color <- "orange"
V(net.bg)$label <- ""
E(net.bg)$arrow.mode <- 0
plot(net.bg)
You can set the layout in the plot function:
plot(net.bg, layout=layout_randomly)
Or you can calculate the vertex coordinates in advance:
l <- layout_in_circle(net.bg)
plot(net.bg, layout=l)
l is simply a matrix of x, y coordinates (\(N \times 2\)) for the N nodes in the graph. You can easily generate your own:
l <- cbind(1:vcount(net.bg), c(1, vcount(net.bg):2))
plot(net.bg, layout=l)
igraph has a number of built-in layouts, including: Random
l <- layout_randomly(net.bg)
plot(net.bg, layout=l)
igraph has a number of built-in layouts, including: Circle
l <- layout_in_circle(net.bg)
plot(net.bg, layout=l)
igraph has a number of built-in layouts, including: Sphere
l <- layout_on_sphere(net.bg)
plot(net.bg, layout=l)
igraph has a number of built-in layouts, including: Fruchterman-Reingold
l <- layout_with_fr(net.bg)
plot(net.bg, layout=l)
igraph has a number of built-in layouts, including: Fruchterman-Reingold
Force-directed layout simulates the graph as a physical system
- Nodes are electrically charged particles that repulse each other when they get too close
- Edges act as springs that attract connected nodes closer together
- Thus,
- Edges are similar in length and cross each other as little as possible
- Nodes which share more connections are closer to each other
Use rescale=FALSE to control the spread of the layout
l <- layout_with_fr(net.bg)
l <- norm_coords(l, ymin=-1, ymax=1, xmin=-1, xmax=1)
par(mfrow=c(1,4), mar=c(0,0,0,0))
plot(net.bg, rescale=F, layout=l*0.4)
plot(net.bg, rescale=F, layout=l*0.6)
plot(net.bg, rescale=F, layout=l*0.8)
plot(net.bg, rescale=F, layout=l*1.0)
